Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402729
Kohei Yoshida, H. Ohsaki, M. Iwase
In this paper, optimality recovery of a control method based on the discrete-time state-dependent Riccati equation (DSDRE) is discussed. The relationship between the DSDRE method and the Hamilton-Jacobi-Bellman equation (HJBE) concerned with optimal control for discrete-time nonlinear systems is addressed briefly. Based on the relationship, the stability of the DSDRE method is investigated with the Lyapunov theorem. As a result, the asymptotical stability holds for the DSDRE strategy is proven for some special case, and the optimality does not hold under infinite-horizon criteria in general. Hence an iterative numerical method is proposed to improve and recover the optimality along a state trajectory obtained by the DSDRE strategy. The method is verified through numerical simulations.
{"title":"Optimality recovery of feedback control system based on discrete-time state dependent riccati equation","authors":"Kohei Yoshida, H. Ohsaki, M. Iwase","doi":"10.1109/CCA.2012.6402729","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402729","url":null,"abstract":"In this paper, optimality recovery of a control method based on the discrete-time state-dependent Riccati equation (DSDRE) is discussed. The relationship between the DSDRE method and the Hamilton-Jacobi-Bellman equation (HJBE) concerned with optimal control for discrete-time nonlinear systems is addressed briefly. Based on the relationship, the stability of the DSDRE method is investigated with the Lyapunov theorem. As a result, the asymptotical stability holds for the DSDRE strategy is proven for some special case, and the optimality does not hold under infinite-horizon criteria in general. Hence an iterative numerical method is proposed to improve and recover the optimality along a state trajectory obtained by the DSDRE strategy. The method is verified through numerical simulations.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121229388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402708
Thomas Hansen, Claus T. Henningsen, Jens J. M. Nielsen, Rasmus Pedersen, John Schwensen, Senthuran Sivabalan, J. A. Larsen, J. Leth
During minimal invasive telesurgery with surgical robots, surgeons rely on their vision to determine the forces applied to tissue. Using an end-effector from da Vinci Surgical Systems a force-feedback control system has been developed, in order to reduce the unnecessary forces applied by the surgeon. To avoid adding any additional hardware, the forces in the system have been estimated on the basis of the existing actuators, using parameter estimation techniques. The inevitable time-delays in the network, which imposes challenges in control design, are also estimated and compensated for within the control design. During tests, it has been shown that it is possible to implement a distributed network controller, which is stable over a range of typical time-delays. This shows, that the applied parameter estimation technique is indeed a viable solution for implementing force-feedback in telesurgery.
{"title":"Implementing force-feedback in a telesurgery environment, using parameter estimation","authors":"Thomas Hansen, Claus T. Henningsen, Jens J. M. Nielsen, Rasmus Pedersen, John Schwensen, Senthuran Sivabalan, J. A. Larsen, J. Leth","doi":"10.1109/CCA.2012.6402708","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402708","url":null,"abstract":"During minimal invasive telesurgery with surgical robots, surgeons rely on their vision to determine the forces applied to tissue. Using an end-effector from da Vinci Surgical Systems a force-feedback control system has been developed, in order to reduce the unnecessary forces applied by the surgeon. To avoid adding any additional hardware, the forces in the system have been estimated on the basis of the existing actuators, using parameter estimation techniques. The inevitable time-delays in the network, which imposes challenges in control design, are also estimated and compensated for within the control design. During tests, it has been shown that it is possible to implement a distributed network controller, which is stable over a range of typical time-delays. This shows, that the applied parameter estimation technique is indeed a viable solution for implementing force-feedback in telesurgery.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116776842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402474
Gilda Pedoto, S. Santaniello, G. Fiengo, L. Glielmo, M. Hallett, P. Zhuang, S. Sarma
Deep brain stimulation (DBS) is a highly promising therapy for Parkinson's disease (PD). However, most patients do not get full therapeutic benefit from DBS yet, due to its critical dependence on electrode location. For this reason, we believe that the investigation of a neural modeling, estimation and control framework for the STN is an interesting research problem. This would pave the way for the development of a novel surgical tool for the DBS placement standardization, i.e., an automated intraoperative closed-loop DBS localization system. A fundamental problem to be solved for the realization of a such framework is the neurophysiologic characterization of the STN activity. Indeed, this would allow to understand if the modeling of the sweet spot is feasible. In this paper an effort towards the modeling of the neuronal activity near the stimulation target is made: first we analyze single unit spiking activity of 120 STN neurons collected from four PD patients at different distances from the sweet spot and, for each neuron, we estimate a point process model (PPM). Then, we see that PPMs capture the stochastic effects of the distance from the sweet spot on the STN spiking activity, and characterize the impact of local neuronal networks on the single neurons. Our results suggest that PPMs might be an effective tool for modeling of the STN neuronal activities accounting for the depth within it.
{"title":"Towards automated navigation of deep brain stimulating electrodes: Analyzing neuronal activity near the target","authors":"Gilda Pedoto, S. Santaniello, G. Fiengo, L. Glielmo, M. Hallett, P. Zhuang, S. Sarma","doi":"10.1109/CCA.2012.6402474","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402474","url":null,"abstract":"Deep brain stimulation (DBS) is a highly promising therapy for Parkinson's disease (PD). However, most patients do not get full therapeutic benefit from DBS yet, due to its critical dependence on electrode location. For this reason, we believe that the investigation of a neural modeling, estimation and control framework for the STN is an interesting research problem. This would pave the way for the development of a novel surgical tool for the DBS placement standardization, i.e., an automated intraoperative closed-loop DBS localization system. A fundamental problem to be solved for the realization of a such framework is the neurophysiologic characterization of the STN activity. Indeed, this would allow to understand if the modeling of the sweet spot is feasible. In this paper an effort towards the modeling of the neuronal activity near the stimulation target is made: first we analyze single unit spiking activity of 120 STN neurons collected from four PD patients at different distances from the sweet spot and, for each neuron, we estimate a point process model (PPM). Then, we see that PPMs capture the stochastic effects of the distance from the sweet spot on the STN spiking activity, and characterize the impact of local neuronal networks on the single neurons. Our results suggest that PPMs might be an effective tool for modeling of the STN neuronal activities accounting for the depth within it.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132369781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402403
J. Belikov, Ü. Kotta, M. Tõnso
The problem of realization of nonlinear multi-input multi-output equations, defined on homogeneous time scale, in the state-space form is studied. The polynomial approach, based on non-commutative ring of skew polynomials, allows to replace the existing step-by-step algorithm based on certain sequences of differential one-forms by explicit formulas to compute the differentials of the state coordinates directly from system description. Implementation details of the developed theory in the Mathematica environment are presented.
{"title":"Explicit formulas for the state coordinates in nonlinear MIMO realization problem on homogeneous time scales","authors":"J. Belikov, Ü. Kotta, M. Tõnso","doi":"10.1109/CCA.2012.6402403","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402403","url":null,"abstract":"The problem of realization of nonlinear multi-input multi-output equations, defined on homogeneous time scale, in the state-space form is studied. The polynomial approach, based on non-commutative ring of skew polynomials, allows to replace the existing step-by-step algorithm based on certain sequences of differential one-forms by explicit formulas to compute the differentials of the state coordinates directly from system description. Implementation details of the developed theory in the Mathematica environment are presented.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"626 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113994333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402444
V. Spinu, A. Oliveri, M. Lazar, M. Storace
This paper proposes a method for FPGA implementation of explicit, piecewise affine (PWA) model predictive control (MPC) laws for non-inverting buck-boost DC-DC converters. A novel approach to obtain a PWA model of the power converter is proposed and two explicit MPC laws are derived, i.e., one based on the standard approach to synthesis of explicit MPC and one based on a simplicial PWA approximation of the resulting MPC law, which permits a more efficient implementation. An FPGA circuit is designed for both the original and the approximating MPC control law. Two hardware architectures with different FPGA footprint and computation latency are developed for each control law. Extensive real-time experiments demonstrate the performance of the two MPC controllers and their computational characteristics.
{"title":"FPGA implementation of optimal and approximate model predictive control for a buck-boost DC-DC converter","authors":"V. Spinu, A. Oliveri, M. Lazar, M. Storace","doi":"10.1109/CCA.2012.6402444","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402444","url":null,"abstract":"This paper proposes a method for FPGA implementation of explicit, piecewise affine (PWA) model predictive control (MPC) laws for non-inverting buck-boost DC-DC converters. A novel approach to obtain a PWA model of the power converter is proposed and two explicit MPC laws are derived, i.e., one based on the standard approach to synthesis of explicit MPC and one based on a simplicial PWA approximation of the resulting MPC law, which permits a more efficient implementation. An FPGA circuit is designed for both the original and the approximating MPC control law. Two hardware architectures with different FPGA footprint and computation latency are developed for each control law. Extensive real-time experiments demonstrate the performance of the two MPC controllers and their computational characteristics.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134213574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402392
A. Pyrkin, A. Bobtsov, S. Kolyubin, A. Vedyakov
The frequency estimation technique with guaranteed finite time of convergence to a given accuracy of identification is presented. The approach for a high frequency noise rejection is proposed. The possibility of switching algorithm introduction for estimation quality improvement is discussed. The proposed solution has order three, that is smaller than in other existent solutions. The dimension of modified robust frequency estimator that can reject the additional measuring noise is equal four. Efficiency of the approach is demonstrated on examples of computer simulation.
{"title":"Precise frequency estimator for noised periodical signals","authors":"A. Pyrkin, A. Bobtsov, S. Kolyubin, A. Vedyakov","doi":"10.1109/CCA.2012.6402392","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402392","url":null,"abstract":"The frequency estimation technique with guaranteed finite time of convergence to a given accuracy of identification is presented. The approach for a high frequency noise rejection is proposed. The possibility of switching algorithm introduction for estimation quality improvement is discussed. The proposed solution has order three, that is smaller than in other existent solutions. The dimension of modified robust frequency estimator that can reject the additional measuring noise is equal four. Efficiency of the approach is demonstrated on examples of computer simulation.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134056475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402643
S. Pfeiffer, G. Lichtenberg, C. Schmidt, H. Schlarb
An iterative learning control (ILC) algorithm reduces repetitive control errors to a desired trajectory within the same repeated task. This paper considers an alternative ILC representation based on a tensor representation. Hereby a decoupling of static and dynamic parts of each calculated ILC matrix leads for computational reasons to a reduction by an order of magnitude. Based on such tensor representation the Norm Optimal ILC is compressed to a Norm Optimal Tensor ILC. The reduced number of elements to store the ILC parameter in this approach simplifies the calculation, especially for high sampled datasets and therefore long trajectories. The resulting algorithm is implemented at FLASH, a free electron laser facility, highly suitable for this approach.
{"title":"Tensor techniques for iterative learning control of a free-electron laser","authors":"S. Pfeiffer, G. Lichtenberg, C. Schmidt, H. Schlarb","doi":"10.1109/CCA.2012.6402643","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402643","url":null,"abstract":"An iterative learning control (ILC) algorithm reduces repetitive control errors to a desired trajectory within the same repeated task. This paper considers an alternative ILC representation based on a tensor representation. Hereby a decoupling of static and dynamic parts of each calculated ILC matrix leads for computational reasons to a reduction by an order of magnitude. Based on such tensor representation the Norm Optimal ILC is compressed to a Norm Optimal Tensor ILC. The reduced number of elements to store the ILC parameter in this approach simplifies the calculation, especially for high sampled datasets and therefore long trajectories. The resulting algorithm is implemented at FLASH, a free electron laser facility, highly suitable for this approach.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115838567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402352
A. Pyrkin, A. Bobtsov, S. Kolyubin, Maxim V. Faronov
We design the output feedback controller for a class of nonlinear systems with a state delay, structural and external disturbances. The external perturbation is bounded. Structural disturbance means the unmodeled and unknown stable dynamics in the control loop. In this paper we consider the problem of exponential stability for disturbed systems which can be represented as a feedback connection of a linear dynamical system with unknown parameters and a uncertain nonlinearity satisfying a sector constraint. Proposed approach is extended for the case of unknown relative degree of the system by modification of the control law.
{"title":"Output controller for uncertain nonlinear systems with structural, parametric, and signal disturbances","authors":"A. Pyrkin, A. Bobtsov, S. Kolyubin, Maxim V. Faronov","doi":"10.1109/CCA.2012.6402352","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402352","url":null,"abstract":"We design the output feedback controller for a class of nonlinear systems with a state delay, structural and external disturbances. The external perturbation is bounded. Structural disturbance means the unmodeled and unknown stable dynamics in the control loop. In this paper we consider the problem of exponential stability for disturbed systems which can be represented as a feedback connection of a linear dynamical system with unknown parameters and a uncertain nonlinearity satisfying a sector constraint. Proposed approach is extended for the case of unknown relative degree of the system by modification of the control law.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131930351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402645
T. Bakka, H. Karimi
This paper deals with output ℌ∞ control synthesis for a linear parameter-varying model of a floating wind turbine. A nonlinear model is introduced and linearized to obtain a linear model for each desired azimuth angle using the wind turbine software FAST. The main contributions of this paper are threefold. Firstly, the family of linear models are represented based on an affine parameter-varying model structure. Secondly, the bounded parameter-varying parameters are removed using upper bounded inequalities in the control design process. Thirdly, the control problem is formulated in terms of linear matrix inequalities (LMIs). The simulation results show a comparison between a controller design based on a constant linear model and a controller based on the linear parameter-varying model. The results show the effectiveness of our proposed design technique.
{"title":"Robust output feedback ℌ∞ control synthesis with pole placement for offshore wind turbine system: An LMI approach","authors":"T. Bakka, H. Karimi","doi":"10.1109/CCA.2012.6402645","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402645","url":null,"abstract":"This paper deals with output ℌ∞ control synthesis for a linear parameter-varying model of a floating wind turbine. A nonlinear model is introduced and linearized to obtain a linear model for each desired azimuth angle using the wind turbine software FAST. The main contributions of this paper are threefold. Firstly, the family of linear models are represented based on an affine parameter-varying model structure. Secondly, the bounded parameter-varying parameters are removed using upper bounded inequalities in the control design process. Thirdly, the control problem is formulated in terms of linear matrix inequalities (LMIs). The simulation results show a comparison between a controller design based on a constant linear model and a controller based on the linear parameter-varying model. The results show the effectiveness of our proposed design technique.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"41 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131715850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-10-01DOI: 10.1109/CCA.2012.6402424
Jin Guo, Yanlong Zhao, Ji-feng Zhang
This paper takes the gain system identification with multi-threshold quantization observations as an example to explore how to select the optimal thresholds and quantized values. A projection recursive algorithm is proposed to estimate the parameter and proved to be both mean-square and almost surely convergent under a class of persistently exciting inputs. The upper bound of the convergence rate is also obtained, which has the same order as the one of optimal estimation in the case where the system output is accurately measured and not quantized. Then, the asymptotic property is analyzed and the optimal scheme of quantization values and thresholds is given by use of the multi-linear transformation. A numerical example is used to demonstrate the effectiveness of the algorithms and the main results obtained.
{"title":"System identification with multi-threshold quantized observations and bounded persistent excitations","authors":"Jin Guo, Yanlong Zhao, Ji-feng Zhang","doi":"10.1109/CCA.2012.6402424","DOIUrl":"https://doi.org/10.1109/CCA.2012.6402424","url":null,"abstract":"This paper takes the gain system identification with multi-threshold quantization observations as an example to explore how to select the optimal thresholds and quantized values. A projection recursive algorithm is proposed to estimate the parameter and proved to be both mean-square and almost surely convergent under a class of persistently exciting inputs. The upper bound of the convergence rate is also obtained, which has the same order as the one of optimal estimation in the case where the system output is accurately measured and not quantized. Then, the asymptotic property is analyzed and the optimal scheme of quantization values and thresholds is given by use of the multi-linear transformation. A numerical example is used to demonstrate the effectiveness of the algorithms and the main results obtained.","PeriodicalId":284064,"journal":{"name":"2012 IEEE International Conference on Control Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133686929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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